cell type compositions scRNA
source("_src/comp_plot.R")
# rank_by()
comp_tbl_sample <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, cell_type) %>%
tally() %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100) %>%
mutate(label_supersite = "Site",
label_therapy = "Rx",
label_mutsig = "Signature") %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
group_by(sample_id) %>%
mutate(ntotal = sum(n)) %>%
filter(ntotal > 0) %>%
ungroup()
comp_tbl_consOV <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, consensusOV) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100,
log10n = log10(n)) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
ungroup
#
# g9 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Immunoreactive")
# g10 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Mesenchymal")
# g11 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Differentiated")
# g12 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Proliferative")
#
# g13 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Immunoreactive")
# g14 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Mesenchymal")
# g15 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Differentiated")
# g16 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Proliferative")
# pdf("_fig/002_cohort/002_comp_full.pdf", width = 3.5, height = 12)
# g1;g2;g3;g4;g5;g6;g7;g8;g9;g10;g11;g12;g13;g14;g15;g16
# dev.off()
Site
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
for(i in 1:length(cell_types_immune)){
cat('### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i])
p <- plot_grid(plist$pbar1, plist$pbar2, plist$pbox2, plist$pvec,
ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
B cell
Plasma cell
T cell
Myeloid cell
Dendritic cell
for(i in 1:length(cell_types_stromal)){
cat('### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i])
p <- plot_grid(plist$pbar1, plist$pbar2, plist$pbox2, plist$pvec,
ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Endothelial cell
Fibroblast
Ov cancer cell
Signature
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
for(i in 1:length(cell_types_immune)){
cat('### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i])
p <- plot_grid(plist$pbar1, plist$pbar2, plist$pbox1, plist$pvec,
ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
B cell
Plasma cell
T cell
Myeloid cell
Dendritic cell
for(i in 1:length(cell_types_stromal)){
cat('### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i])
p <- plot_grid(plist$pbar1, plist$pbar2, plist$pbox1, plist$pvec,
ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Endothelial cell
Fibroblast
Ov cancer cell
summary stats grid
# right_grid_full <- ggdraw() +
# draw_plot(tcga_heat + guides(fill = F), x = -0.05, y = 0.58, width = 0.65, height = 0.4) +
# draw_grob(tcga_heat_legend, x = 0.05, y = 0.56, width = 0.2, height = 0.2) +
# draw_plot(mixing_plot_cell_type, x = 0.56, y = 0.58, width = 0.45, height = 0.4) +
# draw_plot(pcomp_grid_p_full, x = 0, y = 0, width = 0.5, height = 0.55) +
# draw_plot(pcomp_grid_n_full, x = 0.5, y = 0, width = 0.5, height = 0.55)
#
# right_grid_full
#
# ggsave("_fig/001x_right_grid_full.png", right_grid_full, width = 6, height = 10)
# ggsave("_fig/001x_right_grid_full.pdf", right_grid_full, width = 6, height = 10)
# right_grid <- ggdraw() +
# draw_plot(tcga_heat + guides(fill = F), x = -0.05, y = 0.65, width = 0.65, height = 0.35) +
# draw_grob(tcga_heat_legend, x = 0.05, y = 0.6, width = 0.2, height = 0.2) +
# draw_plot(mixing_plot_cell_type, x = 0.56, y = 0.65, width = 0.45, height = 0.35) +
# draw_plot(pcomp_grid_p, x = 0, y = 0, width = 0.5, height = 0.6) +
# draw_plot(pcomp_grid_n, x = 0.5, y = 0, width = 0.5, height = 0.6)
#
# right_grid
#
# ggsave("_fig/001x_right_grid.png", right_grid, width = 6, height = 10)
# ggsave("_fig/001x_right_grid.pdf", right_grid, width = 6, height = 10)
mpIF composition
# ## meta data for mpIF
# mpif_meta_tbl <- read_excel("_data/small/MSK SPECTRUM - mpIF.xlsx", sheet = 3) %>%
# mutate(slide_id = str_replace_all(pici_id, " ", "_"),
# sample_cd45p = paste0(patient_id, "_", surgery, "_CD45P_", str_replace_all(toupper(tumor_subsite), " ", "_")),
# sample_cd45n = paste0(patient_id, "_", surgery, "_CD45N_", str_replace_all(toupper(tumor_subsite), " ", "_"))) %>%
# mutate(tumor_supersite = ifelse(tumor_supersite == "Upper Quadrant", "UQ", tumor_supersite))
#
# # mpif_pixel <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v8/integrate/outputs/cohort_merge/patient/SPECTRUM/all/detection.tsv") %>%
# # select(cell_id, compartment = Parent)
# #
# # mpif_cell_type <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v8/integrate/outputs/cohort_merge/patient/SPECTRUM/all/cell_type_manual.tsv") %>%
# # left_join(mpif_pixel, by = "cell_id") %>%
# # mutate(cell_id = str_remove_all(cell_id, "CD68.TOX.PD1.PDL1.CD8.panCK_CK8-18.DAPI_|_component_data - resolution #1")) %>%
# # separate(cell_id, into = c("patient_id", "tumor_subsite", "fov_id", "cell_idx"),
# # sep = "_", remove = F) %>%
# # mutate(fov_id = paste0(patient_id, "_", tumor_subsite, "_", fov_id),
# # slide_id = paste0(patient_id, "_", tumor_subsite))
# #
# # ## expand for double and triple positive cells (cell type markers)
# # mpif_ncell <- mpif_cell_type %>%
# # # sample_n(10000) %>%
# # mutate(CD68_state_log = CD68_state == "CD68+",
# # CD8_state_log = CD8_state == "CD8+",
# # panCK_state_log = panCK_state == "panCK+") %>%
# # group_by(cell_id) %>%
# # mutate(n_cells = sum(CD68_state_log, CD8_state_log, panCK_state_log)) %>%
# # select(cell_id, n_cells) %>%
# # deframe
# #
# # mpif_cell_type_expanded <- bind_rows(
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# # TOX_state == "TOX+" | PD1_state == "PD1+" | PDL1_state == "PDL1+") %>%
# # mutate(cell_id = paste0(cell_id, "_0"),
# # cell_type = c("Other")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# # TOX_state == "TOX-" & PD1_state == "PD1-" & PDL1_state == "PDL1-") %>%
# # mutate(cell_id = paste0(cell_id, "_0"),
# # cell_type = c("Unknown")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# # CD8_state == "CD8+") %>%
# # mutate(cell_id = paste0(cell_id, "_1"),
# # cell_type = c("CD8+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# # CD68_state == "CD68+") %>%
# # mutate(cell_id = paste0(cell_id, "_2"),
# # cell_type = c("CD68+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# # panCK_state == "panCK+") %>%
# # mutate(cell_id = paste0(cell_id, "_3"),
# # cell_type = c("panCK+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# # CD8_state == "CD8+") %>%
# # mutate(cell_id = paste0(cell_id, "_1"),
# # cell_type = c("CD8+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# # CD68_state == "CD68+") %>%
# # mutate(cell_id = paste0(cell_id, "_2"),
# # cell_type = c("CD68+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# # panCK_state == "panCK+") %>%
# # mutate(cell_id = paste0(cell_id, "_3"),
# # cell_type = c("panCK+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# # CD8_state == "CD8+") %>%
# # mutate(cell_id = paste0(cell_id, "_1"),
# # cell_type = c("CD8+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# # CD68_state == "CD68+") %>%
# # mutate(cell_id = paste0(cell_id, "_2"),
# # cell_type = c("CD68+")),
# # filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# # panCK_state == "panCK+") %>%
# # mutate(cell_id = paste0(cell_id, "_3"),
# # cell_type = c("panCK+"))
# # )
# #
# # mpif_cell_state_expanded <- mpif_cell_type_expanded %>%
# # mutate(cell_state = case_when(
# # cell_type == "CD8+" ~ paste0(CD8_state, TOX_state, PD1_state),
# # cell_type == "CD68+" ~ paste0(CD68_state, PDL1_state),
# # cell_type == "panCK+" ~ paste0(panCK_state, PDL1_state),
# # cell_type == "Unknown" ~ "Unknown",
# # cell_type == "Other" ~ "Other"
# # ))
# #
# # write_tsv(mpif_cell_state_expanded, "/work/shah/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded.tsv")
#
# mpif_cell_state_expanded <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded.tsv") %>%
# select(cell_id, slide_id, fov_id, compartment, cell_type, cell_state, contains("state")) %>%
# left_join(select(mpif_meta_tbl, slide_id, patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, sample_cd45n), by = "slide_id") %>%
# na.omit()
#
# # ## cell type composition
# # mpif_cell_type_n <- mpif_cell_state_expanded %>%
# # group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id, cell_type) %>%
# # tally() %>%
# # group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id) %>%
# # mutate(nrel = n/sum(n)) %>%
# # ungroup()
# #
# # ## cell type composition slide lvl
# # mpif_cell_type_n_slide <- mpif_cell_state_expanded %>%
# # group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, cell_type) %>%
# # tally() %>%
# # group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p) %>%
# # mutate(nrel = n/sum(n)) %>%
# # ungroup()
#
# ## cell state composition fov lvl
# mpif_cell_state_n <- mpif_cell_state_expanded %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id, cell_state, cell_type) %>%
# tally() %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id) %>%
# mutate(nrel = n/sum(n)) %>%
# ungroup()
#
# ## cell state composition slide lvl
# mpif_cell_state_n_slide <- mpif_cell_state_expanded %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, cell_state, cell_type) %>%
# tally() %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p) %>%
# mutate(nrel = n/sum(n)) %>%
# ungroup()
#
# ## cell state composition slide lvl compartment
# mpif_cell_state_n_slide_compartment <- mpif_cell_state_expanded %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, compartment, cell_state, cell_type) %>%
# tally() %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, compartment) %>%
# mutate(nrel = n/sum(n)) %>%
# ungroup()
#
# ## fov tally helper function
# fov_tally <- . %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id) %>%
# mutate(nrel = n/sum(n)) %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, fov_id, cell_type) %>%
# mutate(nrel_ct = sum(nrel)) %>%
# ungroup
#
# ## slide tally helper function
# slide_tally <- . %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p) %>%
# mutate(nrel = n/sum(n)) %>%
# group_by(patient_id, tumor_supersite, tumor_subsite, therapy, sample_cd45p, cell_type) %>%
# mutate(nrel_ct = sum(nrel)) %>%
# ungroup() %>%
# arrange(cell_type != "CD8+", desc(cell_type), nrel_ct) %>%
# mutate(sample_cd45p = ordered(sample_cd45p, levels = unique(sample_cd45p)),
# sort_short_x = "mpIF",
# label_supersite = "Site",
# label_therapy = "Rx",
# label_mutsig = "Signature",
# patient_id_short = str_sub(patient_id, 13, 15)) %>%
# distinct(sample_cd45p, cell_state, .keep_all = T) %>%
# arrange(sort_short_x, cell_type, cell_state) %>%
# group_by(cell_type, cell_state, sort_short_x) %>%
# mutate(rank_T.cell = row_number(sample_cd45p)) %>%
# mutate(rank_T.cell = scales::rescale(rank_T.cell, c(-1, 1))) %>%
# ungroup %>%
# mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite))),
# cell_state = ordered(cell_state, levels = names(clrs$cell_state)))
#
# ## immune state composition fov lvl
# mpif_cell_state_n_imm <- mpif_cell_state_n %>%
# filter(cell_type %in% c("CD68+", "CD8+")) %>%
# fov_tally
#
# ## immune state composition slide lvl
# mpif_cell_state_n_imm_slide <- mpif_cell_state_n_slide %>%
# # filter(cell_type %in% c("CD68+", "CD8+")) %>%
# slide_tally
#
# ## immune state composition slide lvl per tumor
# mpif_cell_state_n_imm_slide_tumor <- mpif_cell_state_n_slide_compartment %>%
# filter(compartment == "Tumor", therapy == "pre-Rx") %>%
# slide_tally %>%
# mutate(sort_short_x = compartment)
#
# ## immune state composition slide lvl per stroma
# mpif_cell_state_n_imm_slide_stroma <- mpif_cell_state_n_slide_compartment %>%
# filter(compartment == "Stroma", therapy == "pre-Rx") %>%
# slide_tally %>%
# mutate(sort_short_x = compartment)
#
# ## CD68 state composition
# mpif_cell_state_n_imm_cd68 <- mpif_cell_state_n %>%
# filter(cell_type %in% c("CD68+")) %>%
# fov_tally
#
# ## CD68 state composition slide lvl
# mpif_cell_state_n_imm_cd68_slide <- mpif_cell_state_n_slide %>%
# filter(cell_type %in% c("CD68+")) %>%
# slide_tally
#
# ## CD8 state composition
# mpif_cell_state_n_imm_cd8 <- mpif_cell_state_n %>%
# filter(cell_type %in% c("CD8+")) %>%
# fov_tally
#
# ## CD8 state composition slide lvl
# mpif_cell_state_n_imm_cd8_slide <- mpif_cell_state_n_slide %>%
# filter(cell_type %in% c("CD8+")) %>%
# slide_tally
#
# # mpIF_cell_clrs <- c(clrs$cell_type, clrs$cluster_label$T.cell)
# # names(mpIF_cell_clrs) <- str_replace_all(names(mpIF_cell_clrs), " ", ".")
mpif vector ranks
# prim_ranks_cd45p_mpif <- filter(mpif_cell_state_n_imm_slide) %>%
# filter((tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Adnexa")
#
# meta_ranks_cd45p_mpif <- filter(mpif_cell_state_n_imm_slide) %>%
# filter(!(tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Non-adnexa")
#
# median_rank_tbl_cd45p_mpif <- bind_rows(distinct(prim_ranks_cd45p_mpif, median_rank, .keep_all = T),
# distinct(meta_ranks_cd45p_mpif, median_rank, .keep_all = T)) %>%
# median_rank_wrapper
#
# rank_tbl_cd45p_mpif <- bind_rows(prim_ranks_cd45p_mpif, meta_ranks_cd45p_mpif) %>%
# mutate(prim_meta = ifelse(tumor_supersite == "Ascites", "Ascites", prim_meta),
# patient_id_short = ordered(patient_id_short, levels = levels(median_rank_tbl_cd45p_mpif$patient_id_short)))
#
# vector_patient_lvls_cd45p_mpif <- levels(rank_tbl_cd45p$patient_id_short)
# vector_patient_lvls_cd45p_mpif[as.logical(1:length(vector_patient_lvls_cd45p_mpif) %% 2)] <- paste0(vector_patient_lvls_cd45p_mpif[as.logical(1:length(vector_patient_lvls_cd45p_mpif) %% 2)], " ")
#
# vector_rank_list_cd45p_mpif <- list(rank_tbl = rank_tbl_cd45p_mpif,
# median_rank_tbl = median_rank_tbl_cd45p_mpif,
# vector_patient_lvls = vector_patient_lvls_cd45p_mpif)
#
# vector_plot_cd45p_mpif <- vector_patient_plot_wrapper(vector_rank_list_cd45p_mpif, c("#ff7f00", "#33a02c"))
#
#
# ## tumor compartment vectors
# prim_ranks_tumor_mpif <- filter(mpif_cell_state_n_imm_slide_tumor, sort_short_x == "Tumor", therapy == "pre-Rx", compartment == "Tumor") %>%
# filter((tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Adnexa")
#
# meta_ranks_tumor_mpif <- filter(mpif_cell_state_n_imm_slide_tumor, sort_short_x == "Tumor", therapy == "pre-Rx", compartment == "Tumor") %>%
# filter(!(tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Non-adnexa")
#
# median_rank_tbl_tumor_mpif <- bind_rows(distinct(prim_ranks_tumor_mpif, median_rank, .keep_all = T),
# distinct(meta_ranks_tumor_mpif, median_rank, .keep_all = T)) %>%
# median_rank_wrapper
#
# rank_tbl_tumor_mpif <- bind_rows(prim_ranks_tumor_mpif, meta_ranks_tumor_mpif) %>%
# mutate(prim_meta = ifelse(tumor_supersite == "Ascites", "Ascites", prim_meta),
# patient_id_short = ordered(patient_id_short, levels = levels(median_rank_tbl_tumor_mpif$patient_id_short)))
#
# vector_patient_lvls_tumor_mpif <- levels(rank_tbl_cd45p$patient_id_short)
# vector_patient_lvls_tumor_mpif[as.logical(1:length(vector_patient_lvls_tumor_mpif) %% 2)] <- paste0(vector_patient_lvls_tumor_mpif[as.logical(1:length(vector_patient_lvls_tumor_mpif) %% 2)], " ")
#
# vector_rank_list_tumor_mpif <- list(rank_tbl = rank_tbl_tumor_mpif,
# median_rank_tbl = median_rank_tbl_tumor_mpif,
# vector_patient_lvls = vector_patient_lvls_tumor_mpif)
#
# vector_plot_tumor_mpif <- vector_patient_plot_wrapper(vector_rank_list_tumor_mpif, c("#ff7f00", "#33a02c"))
#
#
# ## stroma compartment vectors
# prim_ranks_stroma_mpif <- filter(mpif_cell_state_n_imm_slide_stroma, sort_short_x == "Stroma", therapy == "pre-Rx", compartment == "Stroma") %>%
# filter((tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Adnexa")
#
# meta_ranks_stroma_mpif <- filter(mpif_cell_state_n_imm_slide_stroma, sort_short_x == "Stroma", therapy == "pre-Rx", compartment == "Stroma") %>%
# filter(!(tumor_supersite %in% c("Adnexa")), cell_type == "CD8+", cell_state == "CD8+TOX-PD1-") %>%
# mutate(rank = rank_T.cell) %>%
# prim_ranks_wrapper("Non-adnexa")
#
# median_rank_tbl_stroma_mpif <- bind_rows(distinct(prim_ranks_stroma_mpif, median_rank, .keep_all = T),
# distinct(meta_ranks_stroma_mpif, median_rank, .keep_all = T)) %>%
# median_rank_wrapper
#
# rank_tbl_stroma_mpif <- bind_rows(prim_ranks_stroma_mpif, meta_ranks_stroma_mpif) %>%
# mutate(prim_meta = ifelse(tumor_supersite == "Ascites", "Ascites", prim_meta),
# patient_id_short = ordered(patient_id_short, levels = levels(median_rank_tbl_stroma_mpif$patient_id_short)))
#
# vector_patient_lvls_stroma_mpif <- levels(rank_tbl_cd45p$patient_id_short)
# vector_patient_lvls_stroma_mpif[as.logical(1:length(vector_patient_lvls_stroma_mpif) %% 2)] <- paste0(vector_patient_lvls_stroma_mpif[as.logical(1:length(vector_patient_lvls_stroma_mpif) %% 2)], " ")
#
# vector_rank_list_stroma_mpif <- list(rank_tbl = rank_tbl_stroma_mpif,
# median_rank_tbl = median_rank_tbl_stroma_mpif,
# vector_patient_lvls = vector_patient_lvls_stroma_mpif)
#
# vector_plot_stroma_mpif <- vector_patient_plot_wrapper(vector_rank_list_stroma_mpif, c("#ff7f00", "#33a02c"))
# pcomp1 <- comp_plot_wrapper(mpif_cell_state_n_imm_slide, "mpIF", "n", x = "sample_cd45p", facet = F, fill = "cell_state") +
# scale_y_continuous(expand = c(0, 0),
# breaks = c(0, 250000, 500000),
# limits = c(0, 500000),
# labels = c("", "250000", "500000")) +
# expand_limits(y = c(0, 500000))
#
# pcomp2 <- comp_plot_wrapper(mpif_cell_state_n_imm_slide, "mpIF", "nrel", x = "sample_cd45p", facet = F, fill = "cell_state") +
# scale_y_continuous(expand = c(0, 0),
# breaks = c(0, 0.5, 1),
# limits = c(0, 1),
# labels = c("0", "50", "100")) +
# expand_limits(y = c(0, 1))
#
#
# pcomp_grid_p_mpif <- plot_grid(pcomp1, pcomp2,
# comp_label_boxplot(filter(mpif_cell_state_n_imm_slide, cell_state == "CD8+TOX-PD1-"), x = "tumor_supersite", y = "rank_T.cell", facet = F, cts = "mpIF", ct = "CD8+", pvals = F) + remove_xaxis,
# vector_plot_cd45p + remove_guides,
# ncol = 1, align = "v",
# rel_heights = c(0.15, 0.15, 0.25, 0.45))
#
# ggsave("_fig/002_cohort/002_sorted_comp_mpif.pdf", pcomp_grid_p_mpif, width = 3, height = 6)
#
scRNA marker positivity
# # seu_cohort <- read_rds("/work/shah/isabl_data_lake/analyses/16/52/1652/cohort_merged.rdata")
# #
# # scrna_markers <- as_tibble(cbind(cell_id = colnames(seu_cohort), FetchData(seu_cohort, c("cell_type", "sample", "CD68", "PDCD1", "CD274", "TOX", "CD8A", "CD8B", "KRT8", "KRT19")))) %>%
# # mutate(CD68_state = ifelse(CD68 > 0, "CD68+", "CD68-"),
# # CD8_state = ifelse(CD8A > 0 | CD8B > 0, "CD8+", "CD8-"),
# # panCK_state = ifelse(KRT8 > 0 | KRT19 > 0, "panCK+", "panCK-"),
# # TOX_state = ifelse(TOX > 0, "TOX+", "TOX-"),
# # PD1_state = ifelse(PDCD1 > 0, "PD1+", "PD1-"),
# # PDL1_state = ifelse(CD274 > 0, "PDL1+", "PDL1-"))
# #
# # write_tsv(scrna_markers, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/scrna_mpif_marker_expression.tsv")
#
# scrna_markers <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/scrna_mpif_marker_expression.tsv") %>%
# filter(!(CD68_state == "CD68+" & CD8_state == "CD8+"),
# !(CD68_state == "CD68+" & panCK_state == "panCK+"),
# !(CD8_state == "CD8+" & panCK_state == "panCK+")) %>%
# mutate(cell_type_sc = ifelse(CD68_state == "CD68+", "CD68+", ifelse(CD8_state == "CD8+", "CD8+", ifelse(panCK_state == "panCK+", "panCK+", "Other"))),
# cell_state = case_when(
# cell_type_sc == "CD8+" ~ paste0(CD8_state, TOX_state, PD1_state),
# cell_type_sc == "CD68+" ~ paste0(CD68_state, PDL1_state),
# cell_type_sc == "panCK+" ~ paste0(panCK_state, PDL1_state),
# cell_type_sc == "Unknown" ~ "Unknown",
# cell_type_sc == "Other" ~ "Other"
# ))
#
# markers_pos_frac_scrna_celltype <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
# na.omit() %>%
# group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# tally %>%
# group_by(sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(nrel = n/sum(n)) %>%
# select(sample, cell_type_sc, n, nrel, everything()) %>%
# ungroup
#
# markers_pos_frac_scrna_cellstate <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
# na.omit() %>%
# group_by(sample, cell_state, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# tally %>%
# group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(nrel = n/sum(n)) %>%
# select(sample, cell_type_sc, cell_state, n, nrel, everything()) %>%
# ungroup
#
# markers_pos_frac_scrna_gene <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, cell_type, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, -sample, -cell_type), by = "cell_id") %>%
# na.omit() %>%
# select(-contains("state")) %>%
# mutate(cell_type = str_replace_all(cell_type, "\\.", " ")) %>%
# gather(gene, value, -cell_id, -sample, -cell_type, -tumor_supersite,
# -consensus_signature, -patient_id_short, -sort_short_x, -cell_type_sc) %>%
# mutate(gene_state = value > 0) %>%
# group_by(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(n = sum(gene_state),
# nrel = n/length(n)) %>%
# ungroup %>%
# distinct(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x, n, nrel) %>%
# select(sample, gene, n, nrel, everything())
mpIF x scRNA correlation
CD45+ cell type correlation
# markers_pos_scrna_mpif <- mpif_cell_state_n_slide_compartment %>%
# select(sample = sample_cd45p, cell_type_sc = cell_type,
# compartment, n_mpif = n, nrel_mpif = nrel) %>%
# group_by(sample, cell_type_sc, compartment) %>%
# summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
# ungroup() %>%
# left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
# filter(sort_short_x == "CD45+",
# cell_type_sc %in% c("CD8+", "CD68+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_type_sc~compartment, scales = "free"),
# stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
# labs(x = "Fraction in scRNA",
# y = "Fraction in mpIF")
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2)
CD45+ cell state correlation
# markers_pos_scrna_mpif_state <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_cd45p, compartment, cell_type) %>%
# mutate(nrel_state = n/sum(n)) %>%
# select(sample = sample_cd45p, cell_type_sc = cell_type,
# compartment, n_mpif = n, nrel_mpif = nrel_state, cell_state) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45+",
# cell_state %in% c("CD8+TOX-PD1-", "CD8+TOX+PD1+", "CD68+PDL1-", "CD68+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
# geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
# labs(x = "Fraction in scRNA",
# y = "Fraction in mpIF")
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_state) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_state) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2, ncol = 1)
CD45- cell type correlation
# markers_pos_scrna_mpif_cd45n <- mpif_cell_state_n_slide_compartment %>%
# select(sample = sample_cd45p, cell_type_sc = cell_type,
# compartment, n_mpif = n, nrel_mpif = nrel) %>%
# mutate(sample = str_replace_all(sample, "CD45P", "CD45N")) %>%
# group_by(sample, cell_type_sc, compartment) %>%
# summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
# ungroup() %>%
# left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
# filter(sort_short_x == "CD45-",
# cell_type_sc %in% c("panCK+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_type_sc~compartment, scales = "free"),
# stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
# labs(x = "Fraction in scRNA",
# y = "Fraction in mpIF")
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2)
CD45- cell state correlation
# markers_pos_scrna_mpif_state_cd45n <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_cd45p, compartment, cell_type) %>%
# mutate(nrel_state = n/sum(n)) %>%
# select(sample = sample_cd45p, cell_type_sc = cell_type,
# compartment, n_mpif = n, nrel_mpif = nrel_state, cell_state) %>%
# mutate(sample = str_replace_all(sample, "CD45P", "CD45N")) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45-",
# cell_state %in% c("panCK+PDL1-", "panCK+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
# geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
# labs(x = "Fraction in scRNA",
# y = "Fraction in mpIF")
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2, ncol = 1)
